Improving RAG Retrieval
Introduction: The Retrieval Bottleneck The generation phase of RAG receives most of the attention. Developers obsess over prompt engineering, model selection, and temperature settings. But generation quality is fundamentally bounded by retrieval quality. If the retrieved...
Semantic Search Explained
Introduction: Beyond Keyword Matching For decades, information retrieval was dominated by keyword search. Users typed words, and systems returned documents containing those words. This approach works well for known-item searches, where the user knows exactly what terms to...
What is a Vector Database?
Introduction: Databases for the AI Era Traditional databases are designed for exact matching and range queries. They store rows of structured data and retrieve them based on precise conditions. A SQL query like SELECT * FROM products WHERE price < 100 is fast and...
ANN Search Explained
When you search a database of a million vector embeddings for the ones closest to your query, checking every single one is often too slow to be practical at scale. Approximate Nearest Neighbor (ANN) search is the technique that makes vector search fast enough for real...
Text Embeddings Explained
Introduction: Turning Language into Mathematics Text embeddings are the invisible engine behind modern information retrieval. They are the reason a search for "automobile" can find documents about "cars," why a question in English can retrieve answers in Spanish, and why a...
Chunk Overlap Explained
Introduction: The Boundary Problem When a document is split into chunks for embedding and retrieval, a subtle but serious problem emerges at the boundaries. A sentence at the end of one chunk might depend on a sentence at the beginning of the next. A definition in chunk one might be …
Choosing the Right Chunk Size
Introduction: The Chunking Dilemma In a Retrieval-Augmented Generation system, documents must be divided into pieces before they can be embedded and stored. This division, called chunking, is one of the most consequential design decisions in the entire pipeline. Choose chunks...
What is RAG?
Introduction: The Knowledge Gap in Large Language Models Large Language Models are trained on vast corpora of internet text, books, and code. They can write poetry, debug software, and explain quantum mechanics. But they have a critical limitation: their knowledge is frozen...
LangChain Embeddings
Introduction: The Bridge Between Language and Mathematics Embeddings are the invisible foundation of modern retrieval systems. At their core, embeddings are dense numerical vectors that capture the semantic meaning of text, images, or other data types. When you convert a...
How LLMs Understand Natural Language
Language is messy. The same sentence can mean different things depending on context, tone, or who's speaking. Words have multiple meanings, sarcasm flips a sentence's intent entirely, and pronouns can point to almost anything nearby. Yet LLMs handle this messiness remarkably...